Search Results (568)

Background/Objectives: Sepsis detection relies on clinical variables and scoring systems assumed to perform uniformly across hospital settings. However, sepsis phenotype distributions shift between clinical environments, suggesting that variable importance may be setting dependent. This study aimed to quantify service-specific variability in the discriminatory capacity of clinical variables for sepsis detection and to evaluate whether this heterogeneity translates into differential performance of machine learning models compared to traditional clinical scoring systems. Methods: This stratified sub-analysis of the BIAlert Sepsis cohort (203,755 patients; 11,864 sepsis episodes, 2014–2018) evaluated 61 structured quantitative variables across nine hospital services (≥90 sepsis episodes each). Within each service, the Mann–Whitney–Wilcoxon test (p < 0.01, Holm-corrected) assessed differences between septic and non-septic episodes. Five machine learning models (Random Forest/BIAlert, XGBoost, CatBoost, SVM, Neural Network) and three clinical rules (NEWS, SIRS, qSOFA) were evaluated globally and stratified across four clinical environments. Results: The proportion of significant variables ranged from 95.1% in the Emergency Department (58/61) to 37.7% in the Intensive Care Unit (23/61). Lactate was the only universally significant variable (9/9 services). Clinical scoring systems collapsed in Critical Care (qSOFA and NEWS AUC 0.459). BIAlert maintained the highest AUC across all environments (0.975–0.857). The Friedman test confirmed significant differences (χ² = 28.00, p < 0.001), with BIAlert achieving a mean rank of 1.0. Conclusions: The discriminatory capacity of clinical variables for sepsis detection is not uniform across hospital services. ML models, particularly BIAlert, maintained robust performance where fixed-rule scoring systems failed. Full article

The potential health hazards caused by microwave exposure have attracted increasing attention. Microwave radiation has been reported to induce oxidative stress in neural tissues, which is considered one of the primary mechanisms underlying its adverse effects on central nervous system function. The hippocampus is sensitive to microwave radiation, whereas underlying cellular and molecular mechanisms remain incompletely understood. In this study, microwave-exposed mice exhibited significantly impaired performance in the Go/No-go, Y-maze, and novel object recognition tests at 6 h and 7 days post-exposure, indicating deficits in hippocampus-dependent working memory. Single-cell RNA sequencing of hippocampal tissues from control and microwave-exposed mice yielded 94,088 high-quality cells across eight major cell types. Astrocyte sub-clustering identified five transcriptionally distinct subpopulations, with Astrocyte_S100a6 and Astrocyte_Son proportions increased and Astrocyte_Serpinf1 decreased in the radiation group. Analysis of astrocyte transcriptional state transitions showed microwave-exposed astrocytes were preferentially distributed toward terminal reactive states with depletion at early homeostatic nodes. Cell–cell communication analysis revealed increased total interactions and interaction strength following radiation. Astrocyte outgoing signaling was increased for pathways associated with vascular remodeling, phagocytic regulation, and neuroinflammation, while pathways related to trophic support were decreased. Incoming signaling showed increased activity in pathways linked to phagocytic recruitment and inflammatory mediation. Taken together, these findings indicate that microwave exposure is associated with hippocampus-dependent working memory deficits accompanied by transcriptional remodeling of astrocyte subpopulation composition, directional astrocyte state transitions toward reactive phenotypes, and broad alterations in astrocyte-centered intercellular communication, providing a cellular and molecular framework for understanding astrocyte involvement in microwave radiation-associated hippocampal dysfunction. Full article

Background: Acute dehydration impairs performance, but its effects on resting neuromuscular and tissue mechanics are unclear. We tested whether baseline hydration status and exercise-induced sweat loss alter the resting neuromechanical phenotype of the rectus femoris (RF) as well as skin, subcutaneous tissue (subQ), and fascia overlying the RF. Methods: Thirty physically active men were randomized to hydration guidance (EXP) or habitual intake (CON). Hydration was verified weekly using first-morning urine specific gravity (USG), with targets of USG < 1.018 (EXP) and USG > 1.018 (CON). Participants performed continuous cycling at 50% maximal power output (Wmax) until ~3% body mass loss. Shear-wave elastography quantified tissue shear modulus (kPa), and tensiomyography assessed RF twitch-derived contractile properties (Dm, Tc, Tr) before and immediately after exercise. SWE data were analyzed using mixed design repeated-measures ANOVA; TMG outcomes were analyzed using non-parametric tests. Results: Baseline measures did not differ between groups. No significant group, time, or interaction effects were observed for RF muscle, skin, or subQ shear modulus. In contrast, fascia shear modulus showed a significant time effect, while TMG outcomes did not change significantly from pre- to post-exercise (all p > 0.05). Deep fascia showed a significant main effect of time, with decreased shear modulus post-exercise (F(1, 21) = 5.06, p = 0.035, η²p = 0.194; Δ = 1.25 kPa; d = 0.41; 95% CI [0.04, 0.78]), independent of hydration group. Conclusions: Under moderate-intensity cycling with approximately 3% body mass loss, we did not detect significant hydration-group differences or significant pre–post changes in resting RF twitch-derived contractile properties or in RF muscle, skin, and subQ shear modulus. Fascia shear modulus decreased after exercise irrespective of hydration group. These findings should be interpreted cautiously: the study was underpowered to detect small effects, and the fascial finding emerged from an exploratory, layer-specific analysis without correction for multiple comparisons. It should therefore be regarded as preliminary and hypothesis-generating, requiring confirmation in adequately powered, pre-registered studies. Full article

The failure of therapy in muscle invasive bladder cancer (MIBC) is primarily attributed to tumor heterogeneity and therapy resistance. We propose a novel approach targeting interleukin-13 receptor subunit alpha 2 (IL-13Rα2), which is expressed on bladder cancer (BC) cells but absent in normal urothelial cells. We investigated the therapeutic effects of WPD101a immunotoxin (IL-13-DT390) on IL-13Rα2-expressing BC cells in relation to BC cell phenotype and functional characteristics in vitro using both 2-dimensional (2D) and 3-dimensional (3D) models. Cell phenotype and IL-13Rα2 expression were assessed using flow cytometry, immunofluorescence, and Western blot analysis. The biological effects of WPD101a were evaluated by measuring cell viability and proliferation using the MTT, sulforhodamine B (SRB), CellTiter-Glo and Live/Dead assays. Apoptosis was assessed using Annexin V/propidium iodide (PI) staining, and quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis of CASP genes expression. We found that the reference BC cell lines TCC-SUP, JMSU-1 and UM-UC-3 express IL-13Rα2 at various level in contrast to RT-4, HCV-29 and 5637 cells. Cells expressing IL-13Rα2 were sensitive to WPD101a at lower concentrations in the 2D model (0.1 ng/mL) compared to the 3D model (1.0 ng/mL). IL-13Rα2-negative cells remain resistant to the immunotoxin. WPD101a induces apoptosis in BC cells expressing IL-13Rα2 as confirmed by the presence of apoptotic cells, increase the proportion of cells in the subG1 phase, and by the effector CASP3, CASP7, and initiator CASP8, CASP9 genes expression. This study confirmed receptor-dependent cytotoxic effects of WPD101a and the ability and specificity to inhibit growth and apoptosis induction in MIBC cells expressing IL-13Rα2. Full article

Renal-resident macrophages (RMs) are essential regulators of kidney homeostasis and repair, yet the mechanisms governing RM niche regeneration after acute depletion remain poorly defined. To overcome these limitations, we have developed an inducible human CD59- intermedilysin (hCD59-ILY) ablation system, enabling rapid, specific, and reversible depletion of targeted macrophage populations, and subsequent replenishment of RMs, followed by longitudinal scRNA-seq analysis of kidneys at baseline and days 1, 3, and 7 post-ablation. RM ablation triggered a rapid and sustained upregulation of Cx3cl1, predominantly in proximal tubular epithelial cells (PTC1/PTC2), establishing a persistent chemotactic niche signal that coincided with macrophage repopulation. Regenerating RMs transitioned from inflammatory/stress-associated states toward metabolically active and proliferative phenotypes enriched in glycolysis, oxidative phosphorylation, MYC, and cell-cycle programs, with attenuation of canonical inflammatory pathways. Cell–cell communication analysis revealed an early burst of intercellular signaling at day 1, followed by progressive normalization, with fibronectin (Fn1), osteopontin (Spp1), chemokine (Ccl), and amyloid precursor protein (App) axes emerging as key mediators of niche restoration. Transcriptional network analysis identified a conserved regulatory module (Tfe3, Mitf, Hif1a, Myc, Gabpa, Rcor1) coordinating macrophage differentiation and regenerative programming, linking metabolic adaptation to lineage reconstitution. Sub-clustering revealed five dynamically shifting RM subsets with distinct inflammatory, remodeling, proliferative, and surveillance states, reflecting a hierarchical regeneration process. Functional validation using clodronate-mediated depletion in Secreted Phosphoprotein 1 (Spp1) (Opn)-deficient mice demonstrated impaired macrophage repopulation, establishing osteopontin as a critical regulator of RM regeneration. Together, these data define a coordinated epithelial–immune circuit in which Cx3cl1-driven chemotaxis, Spp1-dependent signaling, and a core transcriptional network orchestrate macrophage niche reconstitution and kidney repair following acute immune cell ablation. Full article

Background/Objectives: Polychlorinated biphenyls (PCBs) are persistent environmental contaminants associated with chronic toxicity and neurological dysfunction. PCB 153 is among the most prevalent congeners detected in environmental and dietary matrices. Although transcriptional responses to PCB 153 have been described, its potential association with post-transcriptional regulation remains poorly defined. Here, we performed an exploratory computational RNA-seq splicing analysis of previously generated transcriptomic data from retinoic acid-differentiated SH-SY5Y cells exposed to a sub-cytotoxic concentration of PCB 153. Methods: RNA-seq data were analyzed to identify candidate differentially alternative splicing events (DASEs). Candidate events were further examined for retained intron (RI)-related premature termination codons (PTCs), and potential regulatory interactions, including DASE-RNA-binding protein (RBP) motif enrichment. Results: PCB 153 exposure was associated with differential expression of 32 RNA-binding protein (RBP) encoding genes and with 90 candidate DASEs. Disease enrichment analysis indicates that genes affected by candidate splicing alterations overlapped with gene sets annotated to intellectual disability and related neurodevelopmental phenotypes. Among retained intron events, several were predicted to introduce PTCs, suggesting potential effects on transcript stability or coding potential. Motif enrichment analysis identified positional enrichment of motifs corresponding to CELF2, NUMA1, PRPF8, and RBM22 within DASE-associated regions, nominating these RBPs as putative regulators associated with the observed splicing alterations. Conclusions: This computational study identifies candidate PCB 153-associated splicing alterations and RBP-related regulatory hypotheses in a neuron-like in vitro model, suggesting a potential mechanistic link between PCB 153 and neurodevelopmental dysfunction. Full article

Background/Objectives: The prognostic significance of mucinous colorectal adenocarcinoma (MAC) is controversial. Some studies report good outcomes relative to conventional colorectal adenocarcinoma (CRC) as is similarly described for MACs in, e.g., the breast, lung, pancreas and prostate. However, other studies refute this, proclaiming either no difference or worse outcomes. Herein, we proffer additional insights into the biology of MAC to explain these conflicting findings. Methods: A literature search was undertaken using keywords pertaining to MAC. Archival cases from our database were analyzed to provide context for our findings. Main Findings: The unifying histologic feature of MACs is their >50% content of extracellular mucin, but they should not be viewed as a monolithic entity, as is commonly portrayed in databases. Instead, MAC is a heterogenous disease as defined by histologic and molecular phenotypes. For example, MACs arising from adenoma-like CRC have relatively good outcomes unlike those from traditional serrated adenomas. Likewise, other factors such as histologic grade (grade 1–3), genomics (e.g., BRAF, KRAS, TP53), microsatellite instability (MSI-H, MSI-L), consensus molecular subtypes (CMS1–CMS4), and mucin types (MUC2, MUC5AC) significantly influence prognosis. These pathophysiologic features, demographics (age and sex) and specific anatomic regions/topography (right/left colon/rectum) can be captured and used to improve prognostic stratification. Conclusions: In contrast to previous studies that largely demarcated MAC as a discrete entity, this paper shows the limitations of this approach by highlighting the various sub-entities comprising MAC. Recognition of this heterogeneity may help to inform future treatment algorithms. Full article

Salinity stress remains a major bottleneck for agriculture in arid regions. While Opuntia ficus-indica is known for its resilience, its young cladodes maintain a misleadingly healthy visual appearance and stable biomass even under heavy saline pressure, making traditional vegetation indices and standard statistics unreliable for early diagnosis. The objective of this study was to develop a non-destructive phenotyping framework for the early detection of latent salinity stress in young Opuntia cladodes. Controlled experiments were conducted using hyperspectral data cubes (400–1000 nm) acquired from plants exposed to six distinct salinity levels ranging from 2 to 21 dS m⁻¹. Our methodology integrates these high-dimensional spatial–spectral data with a tailor-made 3D Convolutional Neural Network (3D-CNN). Seven physiological vegetation indices—NDVI, PRI, WI, PSRI, MCARI, SIPI, and NDRE were extracted to track sub-clinical shifts and processed as a volumetric depth dimension within the network to preserve spatial–spectral integrity. The optimized 3D-CNN framework achieved a validation accuracy of 99.7% and a weighted F1-score of 99.1%, delivering 100% precision at critical stress thresholds (13 and 21 dS m⁻¹). Spatial confidence maps (Softmax > 0.95) further confirmed the high reliability of the diagnostic output. Requiring a training duration of approximately 8 s, this framework provides a robust basis for precision early-warning irrigation systems to sustain Opuntia cultivation in challenging environments. Full article

Mungbean is an important legume crop native to India. In this study, 500 indigenous mungbean accessions collected from diverse eco-geographical regions of India were evaluated for agronomic trait genetic variability and core collection development. The accessions were grown in an augmented design during 2019 and 2020, and data were recorded for seven quantitative and 13 qualitative traits. Analysis of variance (ANOVA), frequency distribution, and box-plot analyses revealed substantial phenotypic variation among the accessions. Traits including plant height (PHT), number of pods per plant (NPP), hundred-seed weight (HSW), and single-plant yield (SPY) exhibited high heritability coupled with high genetic advance, indicating the predominance of additive genetic effects. Principal component analysis showed that the first three principal components explained 70% of the total phenotypic variation. The Shannon–Weaver diversity index further indicated high levels of genetic diversity within the population. Based on quantitative traits, the accessions were grouped into six major clusters and 42 sub-clusters, with SPY, NPP, HSW, PHT, and days to 50% flowering (DFF) contributing substantially to genetic divergence. Correlation analysis suggested that direct selection for SPY and indirect selection through associated traits, including NPP, HSW, PHT, NSP, and pod length (POL), may enhance yield improvement. The germplasm collection also possessed desirable traits such as high yield potential, contrasting maturity groups, and plant types suitable for mechanical harvesting and bold-seeded type. A representative core set comprising 50 accessions was developed using the PowerCore program, providing valuable genetic resources for mungbean breeding and genetic improvement programs. Full article

To address the heterogeneity in treatment effects (HTE) in precision medicine for coronary heart disease (CHD), we employed an individualized network analysis framework (Pheno-NM) to elucidate the molecular mechanisms of HTE in patients treated with Danhong injection (DHI). We integrated clinical phenotyping and transcriptomic data to identify three efficacy-based subgroups. The best-responding subgroup (D(+)S(+)) displayed the most complex gene network, with its key hub gene ORM1 linked to platelet activation. Individualized network analysis revealed that patient-specific symptom improvement correlated with unique functional module connectivity and gene expression variations (e.g., HSBP1L1 and KCNG2). Furthermore, six core network topological parameters significantly correlated with treatment efficacy and differed between subgroups (p < 0.05), alongside significant differential expression of genes such as IQCD and MTFR1. This work establishes a novel joint phenotype-genetic network modeling paradigm, providing a molecular framework for HTE and paving the way for precise, personalized cardiovascular interventions by revealing patient-specific network architectures. Full article

Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome characterized by frequent underdiagnosis, diverse etiologies, and limited therapeutic options. Given its complexity, artificial intelligence (AI) and machine learning (ML) offer promising avenues to decode high-dimensional, multi-modal healthcare data. This review aims to synthesize the current landscape of AI/ML applications in HFpEF, evaluating their potential to address critical unmet clinical needs. Methods: We conducted a comprehensive review of the literature focusing on AI/ML paradigms in HFpEF. Key methodological frameworks were examined, including supervised, unsupervised, semi-supervised, and reinforcement learning, alongside advanced techniques such as deep learning and natural language processing (NLP). The analysis focused on the application of these techniques across four domains: diagnosis, sub-phenotyping, risk prediction, and optimization of diagnostic modalities, with specific emphasis on studies incorporating external validation. Results: Current evidence demonstrates that AI approaches effectively enhance diagnostic accuracy and facilitate the identification of distinct HFpEF phenotypes beyond traditional classifications. These technologies show significant utility in refining prognostic assessments and optimizing diagnostic testing strategies. Furthermore, ML-driven analytics provide a robust framework for improving patient selection and streamlining clinical trial design, potentially overcoming historical barriers to drug development in this population. Conclusions: AI represents a transformative tool capable of dissecting the heterogeneity of HFpEF to enable precision medicine. While the potential to improve clinical outcomes is substantial, challenges regarding model interpretability, bias, and clinical integration persist. Future efforts must focus on rigorous external validation and prospective trials to ensure the responsible translation of these technologies into routine clinical practice. Full article

Background/Objectives: The increased prevalence of multidrug-resistant Escherichia coli and carbapenemase-producing Enterobacteriaceae poses a critical threat to global health and food safety. Antimicrobial Blue Light (aBL) in the 400–470 nm spectrum has emerged as a promising, chemical-free disinfection strategy that targets intracellular porphyrins and flavins to induce oxidative stress. However, the influence of wavelength, dosimetry, and environmental stressors on endogenous photoinactivation remains poorly standardized regarding optical parameters and biological exposure protocols. This systematic review aimed to evaluate the antimicrobial efficacy of pure blue light (400–470 nm) against E. coli across various phenotypes and environmental conditions, excluding the use of exogenous photosensitizers. Methods: PubMed, Scopus, and Web of Science were searched for studies that utilized 400–470 nm light as an antimicrobial agent against E. coli. Data extraction focused on spectral efficiency, total fluence (J/cm²), and log₁₀ reduction. The Risk of Bias was assessed using an adapted Office of Health Assessment and Translation tool for in vitro studies. Results: Synthesis of 11 high-quality studies indicated that wavelengths near 405 nm have the highest germicidal efficiency due to the Soret band absorption of endogenous porphyrins. Efficacy is highly dose-dependent: significant log₁₀ reductions were achieved in planktonic cells, although biofilms required substantially higher fluences. Sub-lethal environmental stressors such as acidic pH, high salinity, and thermal fluctuations demonstrated a synergistic effect, which significantly enhanced the rate of photoinactivation. Multidrug-resistant and carbapenemase-producing Enterobacteriaceae strains showed similar susceptibility to aBL relative to antibiotic-sensitive strains, suggesting no cross-resistance between light and traditional drugs. Conclusions: Endogenous blue light is a highly effective, non-thermal technology for E. coli decontamination. Its efficacy is modulated by the interplay between optical parameters and environmental conditions. These findings provide a framework for the development of standardized protocols for applying aBL to clinical wound care and food industry use cases. They also highlight the potential of aBL as a critical tool in the post-antibiotic era. This systematic review was registered in the International prospective register of systematic reviews (PROSPERO) under protocol CRD420261331871. Full article

Background: The in vivo efficacy and mechanisms of matrine (MT) in reversing β-lactam resistance in E. coli remain unclear. Methods: β-lactam-resistant E. coli strains were treated with MT both in vitro and in a murine intestinal colonization model. Phenotypic changes (MIC, morphology, growth, biofilm, β-lactamase) were evaluated, and transcriptomic profiles were analyzed. Results: MT at sub-inhibitory concentrations significantly and concentration-dependently reduced the MICs of β-lactam-resistant E. coli strains by 2- to 32-fold in vitro. This reduction was also confirmed in vivo, and its magnitude became more pronounced as the number of doses increased. MT treatment dispersed bacterial aggregates and dissipated extracellular matrix, but did not alter the morphology of individual bacteria. At concentrations above 1024 μg/mL, MT significantly inhibited bacterial growth; lower concentrations (≤512 μg/mL) had no effect. Notably, MT inhibited biofilm formation and β-lactamase production both in vitro and in vivo. Conclusions: MT restored the susceptibility of β-lactam-resistant E. coli to cefepime and cefquinome. This effect was associated with suppression of biofilm formation and β-lactamase production, which correlated with the downregulation of key genes (ycgR, pgaB, pgaD, bla_TEM and bla_CTX-M). Full article

Parkinson’s disease (PD) is traditionally defined by dopaminergic loss in the substantia nigra, yet its heterogeneous phenotypes and prodromal trajectories challenge a linear, dopamine-centered model. The α-synuclein origin and connectome (SOC) model proposes two major trajectories: a brain-first pathway, with the pathology initiating in limbic and brainstem structures and spreading ipsilaterally to the nigrostriatal system, and a body-first pathway, with the pathology originating in enteric and peripheral autonomic nerves before ascending to the brain. In this review, we integrate the SOC model into a broader framework, reconceptualizing PD as a progressive disorder of spatial–temporal symmetry. Spatial symmetry encompasses left–right and cranio-caudal balance of neural and musculoskeletal systems, whereas temporal symmetry denotes the coherence of biological rhythms from circadian and autonomic cycles, coupled with metabolic health and mitochondrial function, to sub-second timing governed by dopaminergic and basal ganglia–cortical network dynamics. We outline how systemic insulin resistance and mitochondrial stress erode temporal symmetry, while cranio-cervical malalignment and temporomandibular disorders perturb spatial symmetry. We discuss the neurobiological implementation of these symmetry axes via large-scale networks and dopaminergic modulation of spatial–temporal sensorimotor dynamics, framing PD as a multiscale symmetry-breaking process, and explore the implications for symmetry-oriented biomarkers, subtyping, and future interventions. Full article

Background/Objectives: Fall armyworm (FAW) (Spodoptera frugiperda) is a major constraint to maize production in Sub-Saharan Africa, including Mozambique. This study aimed to evaluate maize genotypes for resistance to FAW under greenhouse and field conditions and to assess the association between phenotypic resistance and genomic variation based on single nucleotide polymorphisms (SNPs). Methods: A total of 20 maize genotypes from the Agricultural Research Institute of Mozambique (IIAM) and the International Maize and Wheat Improvement Center (CIMMYT) were evaluated. FAW damage was quantified using the area under the damage progress curve (AUDPC). Phenotypic data were analyzed using ANOVA and mixed models, while molecular analysis was conducted using 10,603 SNP markers located on chromosomes previously associated with FAW resistance. Results: Significant genotypic differences were observed under greenhouse conditions (F = 1.94, p = 0.012) and in the field (p = 0.021), although environmental factors reduced variation in the field. Genotypes such as CML67, CML338, and Kenya amarelo (Acc3550) exhibited consistently lower AUDPC values across environments, indicating stable resistance. However, SNP allele proportion was not significantly associated with phenotypic resistance (r = 0.34, p = 0.147), and regression and ANOVA analyses confirmed the absence of a significant relationship (p > 0.05). Conclusions: FAW resistance in maize is quantitatively inherited and not explained by general genomic variation across candidate regions. Phenotypic screening remains essential, and further studies are required to identify specific loci for effective marker-assisted selection. The identified stable genotypes represent valuable resources for breeding FAW-resistant maize adapted to Mozambique. Full article